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Adaptive Randomized Dimension Reduction on Massive Data
In this paper we develop an approach for dimension reduction that exploits the assumption of low rank structure in high dimensional data to gain both computational and statistical advantages.
Randomized Dimension Reduction on Massive Data
Scalability of statistical estimators is of increasing importance in modern applications and dimension reduction is often used to extract relevant information from data.
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